{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gluon example with DALI\n",
    "\n",
    "## Overview\n",
    "\n",
    "This is a modified [DCGAN example](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html), which uses DALI for reading and augmenting images.\n",
    "\n",
    "## Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os.path\n",
    "import matplotlib as mpl\n",
    "import tarfile\n",
    "import matplotlib.image as mpimg\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import mxnet as mx\n",
    "from mxnet import gluon\n",
    "from mxnet import ndarray as nd\n",
    "from mxnet.gluon import nn, utils\n",
    "from mxnet import autograd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10 # Set low by default for tests, set higher when you actually run this code.\n",
    "batch_size = 64\n",
    "latent_z_size = 100\n",
    "\n",
    "use_gpu = True\n",
    "ctx = mx.gpu() if use_gpu else mx.cpu()\n",
    "\n",
    "lr = 0.0002\n",
    "beta1 = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lfw_url = 'http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz'\n",
    "data_path = 'lfw_dataset'\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "    data_file = utils.download(lfw_url)\n",
    "    with tarfile.open(data_file) as tar:\n",
    "        tar.extractall(path=data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_wd = 64\n",
    "target_ht = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nvidia.dali.pipeline import Pipeline\n",
    "import nvidia.dali.ops as ops\n",
    "import nvidia.dali.types as types\n",
    "import numpy as np\n",
    "\n",
    "class HybridPipe(Pipeline):\n",
    "    def __init__(self, batch_size, num_threads, device_id):\n",
    "        super(HybridPipe, self).__init__(batch_size,\n",
    "                                         num_threads,\n",
    "                                         device_id,\n",
    "                                         seed = 12)\n",
    "        self.input = ops.FileReader(file_root=data_path + \"/lfw-deepfunneled/\", random_shuffle = True)\n",
    "        self.decode = ops.ImageDecoder(device = \"mixed\", output_type = types.RGB)\n",
    "        self.resize = ops.Resize(device = \"gpu\",\n",
    "                                 resize_x = target_wd, resize_y = target_ht,\n",
    "                                 image_type = types.RGB,\n",
    "                                 interp_type = types.INTERP_LINEAR)\n",
    "        self.rotate = ops.Rotate(device = \"gpu\", interp_type = types.INTERP_LINEAR)\n",
    "        self.cmnp = ops.CropMirrorNormalize(device = \"gpu\",\n",
    "                                            output_dtype = types.FLOAT,\n",
    "                                            crop = (target_wd, target_ht),\n",
    "                                            image_type = types.RGB,\n",
    "                                            mean = [127.5, 127.5, 127.5],\n",
    "                                            std = [127.5, 127.5, 127.5])\n",
    "        self.uniform = ops.Uniform(range = (-10., 10.))\n",
    "        self.iter = 0\n",
    "\n",
    "    def define_graph(self):\n",
    "        inputs, labels = self.input(name = \"Reader\")\n",
    "        images = self.decode(inputs)\n",
    "        angle = self.uniform()\n",
    "        images = self.resize(images)\n",
    "        images = self.rotate(images, angle = angle)\n",
    "        output = self.cmnp(images)\n",
    "        return (output)\n",
    "\n",
    "    def iter_setup(self):\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = HybridPipe(batch_size=batch_size, num_threads=4, device_id = 0)\n",
    "pipe.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_out = pipe.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f302e29ceb8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipe_out_cpu = pipe_out[0].as_cpu()\n",
    "img_chw = pipe_out_cpu.at(20)\n",
    "%matplotlib inline\n",
    "plt.imshow((np.transpose(img_chw, (1,2,0))+1.0)/2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nvidia.dali.plugin.mxnet import DALIGenericIterator\n",
    "# recreate pipeline to avoid mixing simple with iterator API\n",
    "pipe = HybridPipe(batch_size=batch_size, num_threads=4, device_id = 0)\n",
    "pipe.build()\n",
    "dali_iter = DALIGenericIterator(pipe, [(\"data\", DALIGenericIterator.DATA_TAG)], pipe.epoch_size(\"Reader\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the generator\n",
    "nc = 3\n",
    "ngf = 64\n",
    "netG = nn.Sequential()\n",
    "with netG.name_scope():\n",
    "    # input is Z, going into a convolution\n",
    "    netG.add(nn.Conv2DTranspose(ngf * 8, 4, 1, 0, use_bias=False))\n",
    "    netG.add(nn.BatchNorm())\n",
    "    netG.add(nn.Activation('relu'))\n",
    "    # state size. (ngf*8) x 4 x 4\n",
    "    netG.add(nn.Conv2DTranspose(ngf * 4, 4, 2, 1, use_bias=False))\n",
    "    netG.add(nn.BatchNorm())\n",
    "    netG.add(nn.Activation('relu'))\n",
    "    # state size. (ngf*8) x 8 x 8\n",
    "    netG.add(nn.Conv2DTranspose(ngf * 2, 4, 2, 1, use_bias=False))\n",
    "    netG.add(nn.BatchNorm())\n",
    "    netG.add(nn.Activation('relu'))\n",
    "    # state size. (ngf*8) x 16 x 16\n",
    "    netG.add(nn.Conv2DTranspose(ngf, 4, 2, 1, use_bias=False))\n",
    "    netG.add(nn.BatchNorm())\n",
    "    netG.add(nn.Activation('relu'))\n",
    "    # state size. (ngf*8) x 32 x 32\n",
    "    netG.add(nn.Conv2DTranspose(nc, 4, 2, 1, use_bias=False))\n",
    "    netG.add(nn.Activation('tanh'))\n",
    "    # state size. (nc) x 64 x 64\n",
    "\n",
    "# build the discriminator\n",
    "ndf = 64\n",
    "netD = nn.Sequential()\n",
    "with netD.name_scope():\n",
    "    # input is (nc) x 64 x 64\n",
    "    netD.add(nn.Conv2D(ndf, 4, 2, 1, use_bias=False))\n",
    "    netD.add(nn.LeakyReLU(0.2))\n",
    "    # state size. (ndf) x 32 x 32\n",
    "    netD.add(nn.Conv2D(ndf * 2, 4, 2, 1, use_bias=False))\n",
    "    netD.add(nn.BatchNorm())\n",
    "    netD.add(nn.LeakyReLU(0.2))\n",
    "    # state size. (ndf) x 16 x 16\n",
    "    netD.add(nn.Conv2D(ndf * 4, 4, 2, 1, use_bias=False))\n",
    "    netD.add(nn.BatchNorm())\n",
    "    netD.add(nn.LeakyReLU(0.2))\n",
    "    # state size. (ndf) x 8 x 8\n",
    "    netD.add(nn.Conv2D(ndf * 8, 4, 2, 1, use_bias=False))\n",
    "    netD.add(nn.BatchNorm())\n",
    "    netD.add(nn.LeakyReLU(0.2))\n",
    "    # state size. (ndf) x 4 x 4\n",
    "    netD.add(nn.Conv2D(1, 4, 1, 0, use_bias=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss\n",
    "loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()\n",
    "\n",
    "# initialize the generator and the discriminator\n",
    "netG.initialize(mx.init.Normal(0.02), ctx=ctx)\n",
    "netD.initialize(mx.init.Normal(0.02), ctx=ctx)\n",
    "\n",
    "# trainer for the generator and the discriminator\n",
    "trainerG = gluon.Trainer(netG.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})\n",
    "trainerD = gluon.Trainer(netD.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:speed: 594.133498594542 samples/s\n",
      "INFO:root:discriminator loss = 1.548318, generator loss = 6.110404, binary training acc = 0.554688 at iter 0 epoch 0\n",
      "INFO:root:speed: 1864.6789758123898 samples/s\n",
      "INFO:root:discriminator loss = 1.875944, generator loss = 10.167295, binary training acc = 0.884360 at iter 100 epoch 0\n",
      "INFO:root:speed: 1925.027473197318 samples/s\n",
      "INFO:root:discriminator loss = 1.383538, generator loss = 11.330649, binary training acc = 0.861046 at iter 200 epoch 0\n",
      "INFO:root:speed: 1916.2053295452113 samples/s\n",
      "INFO:root:discriminator loss = 0.729032, generator loss = 4.568172, binary training acc = 0.812500 at iter 0 epoch 1\n",
      "INFO:root:speed: 1879.8790985615642 samples/s\n",
      "INFO:root:discriminator loss = 0.659974, generator loss = 9.797787, binary training acc = 0.876702 at iter 100 epoch 1\n",
      "INFO:root:speed: 1831.536308618137 samples/s\n",
      "INFO:root:discriminator loss = 0.543448, generator loss = 8.295659, binary training acc = 0.876049 at iter 200 epoch 1\n",
      "INFO:root:speed: 1899.7151935910774 samples/s\n",
      "INFO:root:discriminator loss = 0.256711, generator loss = 5.445935, binary training acc = 0.976562 at iter 0 epoch 2\n",
      "INFO:root:speed: 1952.044911464204 samples/s\n",
      "INFO:root:discriminator loss = 0.231668, generator loss = 5.553924, binary training acc = 0.882890 at iter 100 epoch 2\n",
      "INFO:root:speed: 1897.901949971012 samples/s\n",
      "INFO:root:discriminator loss = 0.370034, generator loss = 6.385639, binary training acc = 0.894123 at iter 200 epoch 2\n",
      "INFO:root:speed: 1894.9009332072114 samples/s\n",
      "INFO:root:discriminator loss = 0.619546, generator loss = 7.575753, binary training acc = 0.898438 at iter 0 epoch 3\n",
      "INFO:root:speed: 1860.8140748802484 samples/s\n",
      "INFO:root:discriminator loss = 0.346177, generator loss = 7.045583, binary training acc = 0.916615 at iter 100 epoch 3\n",
      "INFO:root:speed: 1883.8369054135612 samples/s\n",
      "INFO:root:discriminator loss = 0.223433, generator loss = 5.369461, binary training acc = 0.916123 at iter 200 epoch 3\n",
      "INFO:root:speed: 1907.6398988032633 samples/s\n",
      "INFO:root:discriminator loss = 1.107040, generator loss = 12.463382, binary training acc = 0.734375 at iter 0 epoch 4\n",
      "INFO:root:speed: 1817.1910100189548 samples/s\n",
      "INFO:root:discriminator loss = 0.434032, generator loss = 9.117072, binary training acc = 0.880647 at iter 100 epoch 4\n",
      "INFO:root:speed: 1825.7934486886495 samples/s\n",
      "INFO:root:discriminator loss = 0.400421, generator loss = 4.347582, binary training acc = 0.883590 at iter 200 epoch 4\n",
      "INFO:root:speed: 1824.0937204830084 samples/s\n",
      "INFO:root:discriminator loss = 0.238368, generator loss = 4.456389, binary training acc = 0.984375 at iter 0 epoch 5\n",
      "INFO:root:speed: 1924.4478410174424 samples/s\n",
      "INFO:root:discriminator loss = 0.269836, generator loss = 4.265233, binary training acc = 0.878481 at iter 100 epoch 5\n",
      "INFO:root:speed: 1889.7384423684784 samples/s\n",
      "INFO:root:discriminator loss = 0.418730, generator loss = 4.249841, binary training acc = 0.890081 at iter 200 epoch 5\n",
      "INFO:root:speed: 1938.7360590499716 samples/s\n",
      "INFO:root:discriminator loss = 0.420917, generator loss = 6.589997, binary training acc = 0.945312 at iter 0 epoch 6\n",
      "INFO:root:speed: 1878.9843065335779 samples/s\n",
      "INFO:root:discriminator loss = 0.200992, generator loss = 4.738103, binary training acc = 0.882039 at iter 100 epoch 6\n",
      "INFO:root:speed: 1911.0078879175328 samples/s\n",
      "INFO:root:discriminator loss = 0.275492, generator loss = 4.333203, binary training acc = 0.893151 at iter 200 epoch 6\n",
      "INFO:root:speed: 1927.3771746544605 samples/s\n",
      "INFO:root:discriminator loss = 1.035967, generator loss = 3.340651, binary training acc = 0.664062 at iter 0 epoch 7\n",
      "INFO:root:speed: 1953.3233108968527 samples/s\n",
      "INFO:root:discriminator loss = 0.311858, generator loss = 3.603451, binary training acc = 0.879486 at iter 100 epoch 7\n",
      "INFO:root:speed: 1895.7573977033574 samples/s\n",
      "INFO:root:discriminator loss = 0.267880, generator loss = 3.750516, binary training acc = 0.878304 at iter 200 epoch 7\n",
      "INFO:root:speed: 1954.0342565969063 samples/s\n",
      "INFO:root:discriminator loss = 0.234087, generator loss = 3.313942, binary training acc = 0.968750 at iter 0 epoch 8\n",
      "INFO:root:speed: 1883.2950222752306 samples/s\n",
      "INFO:root:discriminator loss = 0.334708, generator loss = 3.301822, binary training acc = 0.868967 at iter 100 epoch 8\n",
      "INFO:root:speed: 1883.3743027734706 samples/s\n",
      "INFO:root:discriminator loss = 0.510033, generator loss = 2.656876, binary training acc = 0.875894 at iter 200 epoch 8\n",
      "INFO:root:speed: 2009.232385985135 samples/s\n",
      "INFO:root:discriminator loss = 0.412673, generator loss = 3.922147, binary training acc = 0.929688 at iter 0 epoch 9\n",
      "INFO:root:speed: 1885.2260778571379 samples/s\n",
      "INFO:root:discriminator loss = 0.402779, generator loss = 3.599171, binary training acc = 0.877398 at iter 100 epoch 9\n",
      "INFO:root:speed: 1917.683766850742 samples/s\n",
      "INFO:root:discriminator loss = 0.349911, generator loss = 4.183607, binary training acc = 0.882618 at iter 200 epoch 9\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import time\n",
    "import logging\n",
    "\n",
    "real_label = nd.ones((batch_size,), ctx=ctx)\n",
    "fake_label = nd.zeros((batch_size,),ctx=ctx)\n",
    "\n",
    "def facc(label, pred):\n",
    "    pred = pred.ravel()\n",
    "    label = label.ravel()\n",
    "    return ((pred > 0.5) == label).mean()\n",
    "metric = mx.metric.CustomMetric(facc)\n",
    "\n",
    "stamp =  datetime.now().strftime('%Y_%m_%d-%H_%M')\n",
    "logging.basicConfig(level=logging.DEBUG)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    tic = time.time()\n",
    "    btic = time.time()\n",
    "    iter = 0\n",
    "    for batches in dali_iter:  # Using DALI iterator\n",
    "        ############################\n",
    "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
    "        ###########################\n",
    "        data = batches[0].data[0]  # extracting the batch for device 0\n",
    "        latent_z = mx.nd.random_normal(0, 1, shape=(batch_size, latent_z_size, 1, 1), ctx=ctx)\n",
    "\n",
    "        with autograd.record():\n",
    "            # train with real image\n",
    "            output = netD(data).reshape((-1, 1))\n",
    "            errD_real = loss(output, real_label)\n",
    "            metric.update([real_label,], [output,])\n",
    "\n",
    "            # train with fake image\n",
    "            fake = netG(latent_z)\n",
    "            output = netD(fake.detach()).reshape((-1, 1))\n",
    "            errD_fake = loss(output, fake_label)\n",
    "            errD = errD_real + errD_fake\n",
    "            errD.backward()\n",
    "            metric.update([fake_label,], [output,])\n",
    "\n",
    "        trainerD.step(data.shape[0])\n",
    "\n",
    "        ############################\n",
    "        # (2) Update G network: maximize log(D(G(z)))\n",
    "        ###########################\n",
    "        with autograd.record():\n",
    "            fake = netG(latent_z)\n",
    "            output = netD(fake).reshape((-1, 1))\n",
    "            errG = loss(output, real_label)\n",
    "            errG.backward()\n",
    "\n",
    "        trainerG.step(data.shape[0])\n",
    "\n",
    "        # Print log infomation every ten batches\n",
    "        if iter % 100 == 0:\n",
    "            name, acc = metric.get()\n",
    "            logging.info('speed: {} samples/s'.format(batch_size / (time.time() - btic)))\n",
    "            logging.info('discriminator loss = %f, generator loss = %f, binary training acc = %f at iter %d epoch %d'\n",
    "                     %(nd.mean(errD).asscalar(),\n",
    "                       nd.mean(errG).asscalar(), acc, iter, epoch))\n",
    "        iter = iter + 1\n",
    "        btic = time.time()\n",
    "    dali_iter.reset()\n",
    "\n",
    "    name, acc = metric.get()\n",
    "    metric.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize(img_arr):\n",
    "    plt.imshow(((img_arr.asnumpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8))\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n",
      "DEBUG:matplotlib.axes._base:update_title_pos\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_image = 8\n",
    "fig = plt.figure(figsize = (16,8))\n",
    "for i in range(num_image):\n",
    "    latent_z = mx.nd.random_normal(0, 1, shape=(1, latent_z_size, 1, 1), ctx=ctx)\n",
    "    img = netG(latent_z)\n",
    "    plt.subplot(2,4,i+1)\n",
    "    visualize(img[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
